Abstract: Software is used to control complex systems as varied as spacecraft,
land vehicles, life support systems, and factories. Traditional
methods for developing control software do not support the
flexibility, autonomy, and reliability required of these systems,
and often do not support the hierarchical composition of such
systems from independently developed components. For many of these
inherently hybrid (discrete and continuous) systems, combining
traditional feedback PID control with discrete computer-based
controllers is a significant challenge in the design and
implementation of the control systems. This project proposes a new
paradigm, model-enabled control, that uses declarative models
augmented with automatic reasoning systems to support the design,
development, implementation, and validation of controllers for hybrid,
hierarchical systems.

This project will develop techniques for computational modeling and
analysis, and a computational infrastructure to support the autonomous
model-enabled control of multiple hybrid systems. Model-enabled
control is realized by embedding rich computational device models and
associated reasoning and analysis machinery directly into on-line
control systems. Such model-enabled controllers can autonomously adapt
to new high-level task requirements and unanticipated changes in their
environment. They also can detect deterioration or failure of devices
and compensate for such contingencies through reconfiguration of the
remaining components. A multi-level model-enabled control architecture
can provide the machinery for coordination of tasks among multiple
autonomous systems.

The autonomous control of multiple airborne vehicles for space and
earth science, surveillance, and weather mapping is an example of a
complex problem for which traditional simulation and control
techniques are inadequate. Each vehicle in the group or fleet is, of
itself, a complex hybrid dynamic system. Traditional approaches to
the fleet control problem require human operators who determine
mission-level tasks, convert the tasks to detailed commands, and
continually upload the commands to individual vehicles. The result is
inflexible, custom-built, real-time operations with very little
ability to adapt and react to unexpected situations caused by failures
in subsystems, changes to the vehicle environment, and the
introduction of new goals and tasks. Model-enabled control is
particularly suited to the task of autonomous control of multiple
airborne vehicles. We will, therefore, use that problem domain to test
and demonstrate the technology developed in the project.

Developing software in support of model-enabled control presents
diverse computational challenges. The success of our research thus
requires a coupling of expertise from multiple disciplines of science
and engineering including control theory, artificial intelligence,
model-based reasoning, and hybrid systems modeling and control, as
well as disciplinary expertise in the design, modeling, and control of
airborne vehicles. This project is proposed by a multi-disciplinary
team of researchers from the Stanford Computer Science Departmentís
Knowledge Systems Laboratory (KSL), the Stanford Mechanical
Engineering Departmentís Center for Design Research (CDR),
Stanfordís Aeronautics & Astronautics Department (Aero/Astro),
Vanderbiltís Computer Science Department, and an industrial
collaboration with the Systems and Practices Laboratory (SPL) at the
Xerox Palo Alto Research Center.

The proposed approach is to build on the previous work of the team
members. Specifically:

The modeling paradigm is based on compositional and hybrid modeling
techniques pioneered at Stanford KSL and Xerox SPL intermixed with
engineering and control theory modeling techniques from Aero/Astro and
mechanical engineering;

Model-based reasoning about structure and function has been
investigated for many years at Stanford KSL and provides a core for
the model-enabled aspects of the control architecture;

The hybrid control architecture builds on hybrid modeling and
simulation, mode identification, and diagnosis methods for complex
physical systems developed at Vanderbilt;

A new systems architecture for fleets of airborne vehicles has been
developed at Stanford Aero/Astro; and

The use of computer-interpretable design rationale in controller
design has been explored at Stanford CDR.

Our research will bring together these independent strands of research
to produce systems with dramatically enhanced capabilities in
autonomous coordinated control. The results will have impact on those
who design and develop controllers, especially in control of airborne
vehicles, and on those who simulate, diagnose, and verify hybrid
systems. The research will result in new computational capabilities
and tools for both computer scientists, control engineers, and design
engineers.